Using depth information to improve face detection

Burgin, Walker; Pantofaru, Caroline; Smart, William D.

doi:10.1145/1957656.1957690

Cited by 30 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only those images that pass all the stages are positively classified. For further speed improvement, our detector uses depth cues given by the RGBD camera to restrict the search area, as proposed by Burgin et al [23]. In addition, the detector uses depth information to reduce false detection by creating a bounding box around the detected face and validating its size.…”

Section: Face Detectionmentioning

confidence: 99%

SmartWalker: Towards an Intelligent Robotic Walker for the Elderly

Shin

Itten

Rusakov

et al. 2015

2015 International Conference on Intelligent Environments

View full text Add to dashboard Cite

This paper presents SMARTWALKER and evaluates the appropriateness and usefulness of the walker and its gesture-based interface for the elderly. As a high-tech extension of a regular walker, the SMARTWALKER aims to assist its user intelligently and navigate around its environment autonomously. Equipped with sensors and actuators, the prototype accepts gesture commands and navigates around accordingly. The gesture-based interface uses a k-nearest neighbors classifier with dynamic time warping to recognize gestures and the Viola and Jones face detector to locate the user. We evaluated the walker with 23 residents and eight staff members at five different retirement homes in Zürich. The elderly found the SMARTWALKER useful and exciting, but few were willing to replace their walkers by robotic walkers. Their reluctance may stem from the walker's size and weight and their unfamiliarity with technology.

show abstract

Section: Face Detectionmentioning

confidence: 99%

SmartWalker: Towards an Intelligent Robotic Walker for the Elderly

Shin

Itten

Rusakov

et al. 2015

2015 International Conference on Intelligent Environments

View full text Add to dashboard Cite

show abstract

“…The second attempt isolated head and hand tracking. For the head position, we built upon the work of [4,7] and used the characteristics of a human head (size of about 20 cm x 15 cm x 25 cm) to identify it in a predefined area of the depth image. For the hand position, we used a method provided by OpenNI that uses optical flow detection initiated by a waving gesture.…”

Section: Detecting Pointing Gesturesmentioning

confidence: 99%

Free-hand pointing for identification and interaction with distant objects

Rümelin

Marouane²,

Butz³

2013

Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

View full text Add to dashboard Cite

In this paper, we investigate pointing as a lightweight form of gestural interaction in cars. In a pre-study, we show the technical feasibility of reliable pointing detection with a depth camera by achieving a recognition rate of 96% in the lab. In a subsequent insitu study, we let drivers point to objects inside and outside of the car while driving through a city. In three usage scenarios, we studied how this influenced their driving objectively, as well as subjectively. Distraction from the driving task was compensated by a regulation of driving speed and did not have a negative influence on driving behaviour. Our participants considered pointing a desirable interaction technique in comparison to current controller-based interaction and identified a number of additional promising use cases for pointing in the car.

show abstract

“…One further approach of face detection using depth information is reported in [4]. Their method uses depth data from a stereo camera to calculate the corresponding size of faces, applies distance thresholding to avoid detecting faces in the areas that are too far from the camera with too few face pixels.…”

Section: Related Workmentioning

confidence: 99%

Real time face detection using geometric constraints, navigation and depth-based skin segmentation on mobile robots

Masselli

Zell

2012

2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings

View full text Add to dashboard Cite

Abstract-Face detection is an important component for mobile robots to interact with humans in a natural way. Various face detection algorithms for mobile robots have been proposed; however, almost all of them have not yet met the requirements of the accuracy and the speed to run in real time on a robot platform. In this paper, we present a method of combining color and depth images provided by a Kinect camera and navigation information for face detection on mobile robots. This method is shown to be very fast and accurate and runs in real time in indoor environments.

show abstract

Using depth information to improve face detection

Cited by 30 publications

References 4 publications

SmartWalker: Towards an Intelligent Robotic Walker for the Elderly

SmartWalker: Towards an Intelligent Robotic Walker for the Elderly

Free-hand pointing for identification and interaction with distant objects

Real time face detection using geometric constraints, navigation and depth-based skin segmentation on mobile robots

Contact Info

Product

Resources

About